The potential of canonical correlation analysis in multivariable screening of climate model
The statistical downscaling model (SDSM) been used to analyse the potential changes of local climate trend in the long term. The difficulty of the SDSM model in selecting the best predictors group which having good association to the local climate. Even the SDSM provides screening process to analys...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/26036/ http://umpir.ump.edu.my/id/eprint/26036/ http://umpir.ump.edu.my/id/eprint/26036/1/The%20potential%20of%20canonical%20correlation.pdf |
| Summary: | The statistical downscaling model (SDSM) been used to analyse the potential changes of
local climate trend in the long term. The difficulty of the SDSM model in selecting the best predictors group which having good association to the local climate. Even the SDSM provides screening process to analyse the predictor-rainfall relationship, however it has limited ability in analyzing multiple variables from 26 predictors with 10 rainfall stations around Kedah state, Malaysia. In this regard, the Canonical Correlation Analysis (CCA) been used to analyse the multi predictor-rainfall relationships. The concept of canonical coefficient is sufficient to show the capability and reliability of the predictors based on the percentages of variance that can explained in the dependent variable using the independent variable. There were 10 predictors’ group have been developed and one predictor’s group was built based on the CCA result. The performances of these predictors groups were tested using statistical analyses. Results revealed that the predictors group selected by the CCA method has produced smaller values of MAE and MSE for all stations except at station of Ladang Tanjung Pauh. The box plot’s results, which generated from one hundred simulated samples, indicated that the performance of CCA method was remarkable. ical researchers while designing the studies to maximize the benefits of the secondary data. |
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